Protein and lipid biomarkers providing consistent improvement to the prediction of type 2 diabetes

• US 9,217,747 B2
• Filed: 10/28/2010
• Issued: 12/22/2015
• Est. Priority Date: 10/29/2009
• Status: Expired due to Fees

• Current Assignee: True Health Diagnostics LLC (True Health Group LLC)
• Original Assignee: Health Diagnostic Laboratory Incorporated (True Health Group LLC)
• Inventors: McKenna, Michael P., Watkins, Steve M.
• Primary Examiner(s): COOK, LISA V
• Application Number:
  US13/504,720
• Publication Number:
  US 20120328594A1
• Time in Patent Office: 1,881 Days
• US Class Current: 1/1
• CPC Class Codes: A61P 3/10 for hyperglycaemia, e.g. antidiabetics
• CPC Class Codes: G01N 2800/042 Disorders of carbohydrate metabolism, e.g. diabetes, glucose metabolism
• CPC Class Codes: G01N 2800/50 Determining the risk of developing a disease
• CPC Class Codes: G01N 33/66 involving blood sugars, e.g. galactose
• CPC Class Codes: G01N 33/6893 related to diseases not provided for elsewhere
• CPC Class Codes: G01N 33/92 involving lipids, e.g. cholesterol , lipoproteins, or their receptors steroid hormones G01N33/743
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

The invention relates to biomarkers associated with Diabetes, including protein and lipid metabolite biomarkers, methods of using the biomarkers to determine the risk that an individual will develop Diabetes, and methods of screening a population to identify persons at risk for developing Diabetes and other pre-diabetic conditions.

Claims

1. A method of evaluating risk for developing a diabetic condition, the method comprising:

(a) obtaining biomarker measurement data for an individual from at least one biological sample of the individual;

wherein the biomarkers comprise;

(i) glucose, (ii) protein biomarkers adiponectin, ferritin, and insulin, and (iii) at least one lipid metabolite selected from the lipid metabolites in Table 2; and

(b) evaluating the individual'"'"'s risk for developing a diabetic condition based on an output from a model, wherein the model is executed based on an input of the biomarker measurement data;

or

wherein the model comprises the measurements of biomarkers by fitting data from a longitudinal study of a population of individuals and the fitted data comprises levels of the biomarkers and conversion to Diabetes in the longitudinal study of the population of individuals.

2. The method of claim 1, wherein the individual has not been previously diagnosed as having Diabetes, pre-Diabetes, or a pre-diabetic condition.

3. The method of claim 1, wherein the individual has a pre-diabetic condition, and the method evaluates the individual'"'"'s risk for developing Diabetes.

4. The method of claim 1, wherein the individual is pregnant.

5. The method of claim 1, wherein the diabetic condition is selected from the group consisting of Type 2 Diabetes, pre-Diabetes, Metabolic Syndrome, Impaired Glucose Tolerance, and Impaired Fasting Glycemia.

6. The method according to claim 1, wherein the method has an area under the ROC curve, reflecting the degree of diagnostic accuracy for predicting development of the diabetic condition, of at least 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.10, 0.11, 0.12, 0.13, 0.14, or 0.15 greater than a corresponding method using only glucose and the protein biomarkers but not the lipid metabolites as the biomarkers.

13. The method of claim 1, wherein the protein biomarkers further comprise one or more of:

C-reactive protein (CRP), and IL2RA.

14. The method of claim 1, wherein the at least one lipid metabolite is selected from the group consisting of:

CE18;

2n6, CE16;

1n7, CE18;

2n6, CE20;

3n6, CE16;

0, LY18;

2n6, LY18;

1n7, and LY18;

1n9.

15. The method of claim 1, wherein the biomarkers comprise at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten lipid metabolites from Table 2.

17. A method of prophylaxis for Diabetes comprising:

evaluating an individual'"'"'s risk for developing a diabetic condition according to the method of claim 1; and

treating the individual, wherein the individual is at an elevated risk for a diabetic condition, with a treatment regimen to delay or prevent the onset of Diabetes.

18. A method of ranking or grouping a population of individuals, comprising:

calculating a risk for developing a diabetic condition according to the method of claim 1 for individuals comprised within the population; and

ranking individuals within the population relative to the remaining individuals in the population or dividing the population into at least two groups, based on factors comprising the risk for developing a diabetic condition.

19. The method of claim 18, further comprising using ranking data representing the ranking or grouping of the population of individuals for one or more of the following purposes:

to determine an individual'"'"'s eligibility for health insurance;

to determine an individual'"'"'s premium for health insurance;

to determine an individual'"'"'s premium for membership in a health care plan, health maintenance organization, or preferred provider organization; and

to assign health care practitioners to an individual in a health care plan, health maintenance organization, or preferred provider organization.

7. A kit comprising reagents for measuring a group of biomarkers, wherein the biomarkers comprise:

(i) glucose,

(ii) protein biomarkers adiponectin, ferritin, and insulin, and

(iii) at least one lipid metabolite selected from the lipid metabolites in Table 2.

8. A computer readable medium having computer executable instructions for evaluating an individual'"'"'s risk for developing a diabetic condition, the computer readable medium comprising:

a routine, stored on the computer readable medium and adapted to be executed by a processor, to store biomarker measurement data representing measurements of at least the following;

(i) glucose, (ii) protein biomarkers adiponectin, ferritin, and insulin, and (iii) at least one lipid metabolite selected from the lipid metabolites in Table 2; and

a routine stored on the computer readable medium and adapted to be executed by a processor to analyze the biomarker measurement data of the individual to evaluate a risk for developing a diabetic condition.

9. A medical diagnostic test system for evaluating risk for developing a diabetic condition, the system comprising:

a data collection tool adapted to collect biomarker measurement data representative of measurements of biomarkers in at least one biological sample from the individual, wherein the biomarkers comprise;

(i) glucose, (ii) protein biomarkers adiponectin, ferritin, and insulin, and (iii) at least one lipid metabolite selected from the lipid metabolites in Table 2; and

an analysis tool comprising a statistical analysis engine adapted to generate a representation of a correlation between a risk for developing a diabetic condition and measurements of the biomarkers, wherein the representation of the correlation is adapted to be executed to generate a result; and

an index computation tool adapted to analyze the result to determine the individual'"'"'s risk for developing a diabetic condition and represent the result as an index value.

10. A method of developing a model for evaluating an individual'"'"'s risk for developing a diabetic condition, the method comprising:

obtaining biomarker measurement data, wherein the biomarker measurement data is representative of measurements of biomarkers from a population and includes endpoints of the population;

wherein the biomarkers for which measurement data is obtained comprise;

(i) glucose, (ii) protein biomarkers adiponectin, ferritin, and insulin, and (iii) at least one lipid metabolite selected from the lipid metabolites in Table 2;

inputting the biomarker measurement data of at least a subset of the population into a model; and

training the model for endpoints using the inputted biomarker measurement data to derive a representation of a correlation between a risk of developing a diabetic condition and measurements of biomarkers in at least one biological sample from the individual.

11. A method of evaluating an individual'"'"'s current diabetic condition, the method comprising:

obtaining biomarker measurement data from at least one biological sample of the individual, wherein the biomarkers comprise;

(i) glucose, (ii) protein biomarkers adiponectin, ferritin, and insulin, and (iii) at least one lipid metabolite selected from the lipid metabolites in Table 2; and

evaluating the current diabetic condition of the individual based on an output from a model, wherein the model is executed based on an input of the biomarker measurement data.

12. A method of evaluating a diabetic disease surrogate endpoint for an individual, the method comprising:

obtaining biomarker measurement data from at least one biological sample of the individual;

wherein the biomarkers comprise;

(i) glucose, (ii) protein biomarkers adiponectin, ferritin, and insulin, and (iii) at least one lipid metabolite selected from the lipid metabolites in Table 2; and

evaluating a diabetic disease surrogate endpoint in the individual based on an output from a model, wherein the model is executed based on an input of the biomarker measurement data.

16. A method of evaluating an individual'"'"'s current diabetic condition comprising:

obtaining biomarker measurement data from at least one biological sample of the individual, wherein the biomarkers comprise;

(i) glucose, (ii) protein biomarkers adiponectin, ferritin, and insulin, and (iii) at least one lipid metabolite selected from the lipid metabolites in Table 2; and

evaluating the current status of a diabetic condition in the individual based on an output from a model, wherein the model is executed based on an input of the biomarker measurement data.

20. A method of prophylaxis for Diabetes comprising:

obtaining a Diabetes risk score for an individual, wherein the Diabetes risk score is computed according to the method of claim 16 for calculating a risk of developing a diabetic condition; and

generating prescription treatment data representing a prescription for a treatment regimen to delay or prevent the onset of Diabetes to an individual identified by the Diabetes risk score as being at elevated risk for Diabetes.